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AI is now being used in our everyday life and work — doing research, writing emails, summarizing reports, even tutoring kids. But there’s one growing concern bubbling below the idyllic machine surface: AI hallucinations.
Haven’t heard of them? Well, these are moments when AI confidently produces false or misleading information, while sounding completely convincing as it does it. And that’s because there’s a super important distinction between generating content and answering questions.
From inventing academic citations to rewriting the rules of chess, hallucinations reveal the very human-like flaws of these seemingly superhuman tools.
So why do hallucinations happen? How often can you expect them and what can you do about it? Here’s what we know.
What Are AI Hallucinations?
When an AI model “hallucinates” it means it provides inaccurate, fabricated, or nonsensical information while still sounding plausible. And it’s not an error in coding or hardware — it’s a byproduct of how large language models (LLMs) are built.
To put it simply, these systems (ChatGPT, Claude, Gemini, etc.) will predict the most likely next word or phrase based on massive amounts of data they’ve been trained on. But that prediction engine doesn’t actually know if what it's generating is 100% true. When the system is unsure — or given a vague or unsolvable request — it might just make something up.
Why? Well, it doesn’t want to admit it has no answer. Seriously.
Why AI Gets It Wrong
Before we blame the technology’s ego, we have to address that AI models aren’t actually fact-checkers or thinkers. They’re advanced pattern matchers. As IBM puts it, “AI doesn’t reason or verify facts. It generates plausible language based on probabilities.”
That means if a prompt contains flawed assumptions, or if the model hasn’t seen accurate examples of that type of query in its training data, it may guess wrong. And it will often do so with absolute confidence.
Our very own Fabian Miranda, Studio Director & Head of Innovation, shared a great example: “A friend told me he was helping his son with his math homework one night when they came across a question that just didn’t seem to make sense,” Fabian explained. “So his son tried using ChatGPT to answer it. And ChatGPT did with a response that looked right. While his son was convinced it might be right, he wasn’t so sure. After talking to the professor who wrote the equation, it turned out the question itself was wrong — so the answer was completely wrong too. But AI couldn’t verify that (or admit it) and so it gave a confidently incorrect response anyway. Instead of admitting it didn’t know, it made something up.”
In other domains, like engineering or science, where the stakes are much higher, they don’t even take the risk. “That same friend works in civil engineering, building drilling platforms in the ocean. And he admitted that he doesn’t see himself using AI to calculate measurements because mathematical precision is of the utmost importance, and it’s just simply not reliable enough.”
When AI Cheats — or Hallucinates Under Pressure
Sometimes hallucinations go beyond innocent errors. Tech Radar shared a study revealing that certain advanced AI models resort to cheating tactics when about to lose in games like chess. It found that when AI models were given no way to win in a game of chess, they altered the rules of the game just to avoid losing. The models hallucinated legal moves that didn’t exist. In other words, the AI cheated to fulfill the user’s prompt.
This reflects a deeper truth: AI is designed to produce something, anything — even if it means bending reality. Regardless of what you’re asking, it will try to complete the request, even if it has to invent facts along the way. Wild, right?
How Often Do Hallucinations Happen?
There’s no universal statistic here. The hallucination rate varies depending on the task, model, and domain. But here’s the breakdown:
- Summarization tasks: hallucinations occur up to 25–30% of the time.
- Fact-based queries (e.g., history or science): these tend to be more accurate but will show occasional issues, especially with niche or contradictory topics.
- Creative writing or brainstorming: Honestly, hallucinations are probably irrelevant — heck, they may even be helpful.
According to MIT Sloan, hallucinations are a key reason why OpenAI, Google, and others place disclaimers on their models, reminding users to verify all output before use — especially in client or production settings. But the question is: have we been paying enough attention to them?
How You Can Tell if AI is Wrong
Regardless of how you’re using AI, it can be super helpful in terms of cutting down repetitive tasks, sparking brainstorms, and reducing research time. But if you’re relying on it fully — without ANY human review — you’re just looking for trouble.
Whenever you’re using Gen AI, always take it with a sense of skepticism, especially when accuracy is priority. If something sounds off or overly specific, chances are, it probably needs a second look.
When it comes down to it, inspiration, ideation, and mockups are really the best ways to use these tools — at least for right now. Stay away from high-precision tasks. AI is a great collaborator for innovation and creativity, but it’s not a replacement. It still needs serious supervision.
What You Can Do to Stay Ahead of AI Hallucinations
One of the easiest ways to confirm if it is a hallucination is to do a quick fact check with a trusted source. Here are some other helpful tips to minimize the risk and get the most from your AI tools:
- Fact-check a lot, especially stats, quotes, and citations.
- Ask for sources, and cross-reference them outside of the AI tool.
- Use trusted plugins or integrations that allow deep search or citation retrieval (e.g. Bing, Perplexity).
- Avoid vague or speculative prompts that may increase hallucination risk.
- Treat AI output as a draft, not a final deliverable.
- Stay up-to-date on model updates and release notes—performance is evolving fast.
Final Thoughts
AI is an incredible tool — but it’s still far from flawless. The good news is hallucinations aren’t just a slap-on-a-disclaimer-and-move-on issue. The industry is tackling hallucinations in several different ways — from improving the quality and structure of training data to including confidence levels, footnotes, or source citations to help users judge reliability.
But as users, understanding hallucinations is super important to using these tools responsibly, especially in work that requires precision, accountability, or public trust. While the future of AI models may be more and more reliable, the best results now come from a human-AI partnership — where curiosity, critical thinking, and careful review are your greatest assets. So use them wisely!
Sources:
https://www.ibm.com/think/topics/ai-hallucinations https://www.techradar.com/computing/artificial-intelligence/it-turns-out-chatgpt-o1-and-deepseek-r1-cheat-at-chess-if-theyre-losing-which-makes-me-wonder-if-i-should-i-should-trust-ai-with-anything https://mitsloanedtech.mit.edu/ai/basics/addressing-ai-hallucinations-and-bias/ https://www.cnet.com/tech/hallucinations-why-ai-makes-stuff-up-and-whats-being-done-about-it/